Abstract:
A novel method is presented and explored within the framework
of Potts neural networks for solving optimization problems
with a non-trivial topology, with the airline crew scheduling
problem as a target application. The key ingredient to handle
the topological complications is a propagator defined in
terms of Potts neurons. The approach is tested on artificial
problems generated with two real-world problems as templates.
The results are compared against the properties of the
corresponding unrestricted problems. The latter are subject
to a detailed analysis in a companion paper [LU TP 97-11].
Very good results are obtained for a variety of problem
sizes. The computer time demand for the approach only grows
like (number of flights)3. A realistic problem
typically is solved within minutes, partly due to a prior
reduction of the problem size, based on an analysis of the
local arrival/departure structure at the single
airports.